## Problem A delivery robot moves on an `m x n` grid. It starts at `(0, 0)` and must reach `(m-1, n-1)`. Some cells are blocked (obstacles). The robot can move up, down, left, or right one cell per step. Find the shortest path (minimum steps). If no path exists, return -1. If a path exists, also return one valid path as a list of (row, col) tuples. ```python def shortest_delivery( grid: list[list[int]] # 0=open, 1=obstacle ) -> tuple[int, list[tuple[int, int]]]: # returns (steps, path) or (-1, []) pass ``` **Example:** ``` grid = [ [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0] ] Output: (5, [(0,0),(0,1),(1,1),(1,2),(2,3)]) -- one valid path ``` ## Follow-ups 1. Why is BFS guaranteed to find the shortest path on an unweighted grid? 2. How would you modify this for a weighted grid where each cell has a traversal cost? (Dijkstra) 3. What if the robot can break through at most `k` obstacles? How does your state space change? 4. If the grid is dynamically updated (obstacles appear/disappear), how would you replan efficiently?

InterviewDB Experience

Delivery Bot: Simulate a Grid-Based Delivery Robot with Obstacle Avoidance

Interview Experience

Problem A delivery robot moves on an m x n grid. It starts at (0, 0) and must reach (m-1, n-1). Some cells are blocked (obstacles). The robot can move up, down, left, or right one cell per step. Find the shortest path (minimum steps). If no path exists, return -1. If a path exists, also return one valid path as a list of (row, col) tuples. Example: Follow-ups Why is BFS guaranteed to find the shortest path on an unweighted grid? How would you modify this for a weighted grid where each cell has a…

Full Details

🔒

Unlock all Karat questions

Full insider details, leaked discussions, and candidate experiences.

Get full access — from $50/mo

Topics

Coding Graph Matrix

← All Karat questions Karat Software Engineer Questions → All Graph Questions →

About Karat Interview Reports

This question was reported by a candidate who interviewed at Karat. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Karat are the higher-signal extractions to take from this report.

For broader preparation context, the Karat interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.

How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Karat reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Karat Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Karat reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.